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Google GCP-DE Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Operationalizing data and ML pipelines30%- Pipeline automation and orchestration
  • 1. Scheduling and monitoring pipelines
    • 2. Cloud Composer workflows
      - Monitoring and troubleshooting
      • 1. Performance optimization and debugging
        • 2. Logging and observability
          Topic 2: Building and operationalizing data processing systems30%- Data pipeline implementation
          • 1. Dataflow pipeline development
            • 2. Dataproc and Spark-based processing
              - Data ingestion and transformation
              • 1. ETL/ELT workflows
                • 2. Pub/Sub streaming ingestion
                  Topic 3: Maintaining and optimizing data and ML solutions20%- Security and governance
                  • 1. Data encryption and compliance
                    • 2. IAM and access control
                      - Machine learning integration
                      • 1. BigQuery ML usage
                        • 2. Vertex AI integration for pipelines
                          Topic 4: Designing data processing systems20%- Data pipeline architecture design
                          • 1. Scalable data ingestion design
                            • 2. Batch vs streaming data processing selection
                              - Storage and data modeling
                              • 1. Data lake architecture on Google Cloud Storage
                                • 2. Data warehouse design using BigQuery

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                                  Google Data Engineer Sample Questions (Q45-Q50):

                                  NEW QUESTION # 45
                                  You are building a new application that you need to collect data from in a scalable way. Data arrives continuously from the application throughout the day, and you expect to generate approximately 150 GB of JSON data per day by the end of the year. Your requirements are: Decoupling producer from consumer Space and cost-efficient storage of the raw ingested data, which is to be stored indefinitely Near real-time SQL query Maintain at least 2 years of historical data, which will be queried with SQ Which pipeline should you use to meet these requirements?

                                  Answer: B


                                  NEW QUESTION # 46
                                  You need to choose a database for a new project that has the following requirements:
                                  Fully managed
                                  Able to automatically scale up
                                  Transactionally consistent
                                  Able to scale up to 6 TB
                                  Able to be queried using SQL Which database do you choose?

                                  Answer: C


                                  NEW QUESTION # 47
                                  You work for an economic consulting firm that helps companies identify economic trends as they happen. As part of your analysis, you use Google BigQuery to correlate customer data with the average prices of the 100 most common goods sold, including bread, gasoline, milk, and others. The average prices of these goods are updated every 30 minutes. You want to make sure this data stays up to date so you can combine it with other data in BigQuery as cheaply as possible. What should you do?

                                  Answer: B


                                  NEW QUESTION # 48
                                  Which row keys are likely to cause a disproportionate number of reads and/or writes on a particular node in a Bigtable cluster (select 2 answers)?

                                  Answer: A,D

                                  Explanation:
                                  using a timestamp as the first element of a row key can cause a variety of problems.
                                  In brief, when a row key for a time series includes a timestamp, all of your writes will target a single node; fill that node; and then move onto the next node in the cluster, resulting in hotspotting.
                                  Suppose your system assigns a numeric ID to each of your application's users. You might be tempted to use the user's numeric ID as the row key for your table. However, since new users are more likely to be active users, this approach is likely to push most of your traffic to a small number of nodes. [https://cloud.google.com/bigtable/docs/schema-design] Reference:
                                  https://cloud.google.com/bigtable/docs/schema-design-time-series#ensure_that_your_row_key_avoids_hotspotti


                                  NEW QUESTION # 49
                                  Which is not a valid reason for poor Cloud Bigtable performance?

                                  Answer: B

                                  Explanation:
                                  The Cloud Bigtable cluster doesn't have enough nodes. If your Cloud Bigtable cluster is overloaded, adding more nodes can improve performance. Use the monitoring tools to check whether the cluster is overloaded.
                                  Reference: https://cloud.google.com/bigtable/docs/performance


                                  NEW QUESTION # 50
                                  ......

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